Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

Fabienne Windel; Rémy Marc M. Gardier; Gaspard Fourchard; Roser Viñals; Daphne Bavelier; Frank Johannes Padberg; Elmars Rancans; Omer Bonne; Mor Nahum; Jean Philippe Thiran; Takuya Morishita; Friedhelm Christoph Hummel

doi:10.1016/j.clinph.2023.06.009

Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

Fabienne Windel, Rémy Marc M. Gardier, Gaspard Fourchard, Roser Viñals, Daphne Bavelier, Frank Johannes Padberg, Elmars Rancans, Omer Bonne, Mor Nahum, Jean Philippe Thiran, Takuya Morishita, Friedhelm Christoph Hummel^*

^*Corresponding author for this work

School of Occupational Therapy

Research output: Contribution to journal › Article › peer-review

Abstract

Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-to-use digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants’ placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients’ daily lives.

Original language	American English
Pages (from-to)	57-67
Number of pages	11
Journal	Clinical Neurophysiology
Volume	153
DOIs	https://doi.org/10.1016/j.clinph.2023.06.009
State	Published - Sep 2023

Bibliographical note

Funding Information:
We acknowledge access to the facilities and expertise of the MRI facility of the Human Neuroscience Platform (HNP) of the Foundation Campus Biotech Geneva supported by the University of Geneva, Geneva University Hospitals and the École Polytechnique Fédérale de Lausanne (EPFL). We further thank Sylvain Harquel for his input regarding the statistical analysis of the data.

Funding Information:
This work was supported by ERA-NET NEURON (The DiSCoVer project). The NEURON ‘Network of European Funding for Neuroscience Research is established under the organization of the ERA-NET ‘European Research Area Networks’ of the European Commission. National funding agencies are the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF] ) for LMU Munich, the Ministry of Health (MOH) for HUJI and Hadassah, the Swiss National Science Foundation (SNSF) for UNIGE and EPFL and the State Education and Development Agency (VIAA) of Latvia for RSU; the Defitech Foundation (Morges, Switzerland) and by the Bertarelli Foundation (Catalyste Program, Gstaad, Switzerland).

Publisher Copyright:
© 2023

Keywords

Computer vision
Electrode localization algorithm
Home-based non-invasive brain stimulation
Monitoring
Real-time feedback
tES

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10.1016/j.clinph.2023.06.009

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Windel, F., Gardier, R. M. M., Fourchard, G., Viñals, R., Bavelier, D., Padberg, F. J., Rancans, E., Bonne, O., Nahum, M., Thiran, J. P., Morishita, T., & Hummel, F. C. (2023). Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation. Clinical Neurophysiology, 153, 57-67. https://doi.org/10.1016/j.clinph.2023.06.009

@article{558d6ff6caa743ca95321398911b745c,

title = "Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation",

abstract = "Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-to-use digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants{\textquoteright} placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients{\textquoteright} daily lives.",

keywords = "Computer vision, Electrode localization algorithm, Home-based non-invasive brain stimulation, Monitoring, Real-time feedback, tES",

author = "Fabienne Windel and Gardier, {R{\'e}my Marc M.} and Gaspard Fourchard and Roser Vi{\~n}als and Daphne Bavelier and Padberg, {Frank Johannes} and Elmars Rancans and Omer Bonne and Mor Nahum and Thiran, {Jean Philippe} and Takuya Morishita and Hummel, {Friedhelm Christoph}",

note = "Funding Information: We acknowledge access to the facilities and expertise of the MRI facility of the Human Neuroscience Platform (HNP) of the Foundation Campus Biotech Geneva supported by the University of Geneva, Geneva University Hospitals and the {\'E}cole Polytechnique F{\'e}d{\'e}rale de Lausanne (EPFL). We further thank Sylvain Harquel for his input regarding the statistical analysis of the data. Funding Information: This work was supported by ERA-NET NEURON (The DiSCoVer project). The NEURON {\textquoteleft}Network of European Funding for Neuroscience Research is established under the organization of the ERA-NET {\textquoteleft}European Research Area Networks{\textquoteright} of the European Commission. National funding agencies are the Federal Ministry of Education and Research (Bundesministerium f{\"u}r Bildung und Forschung [BMBF] ) for LMU Munich, the Ministry of Health (MOH) for HUJI and Hadassah, the Swiss National Science Foundation (SNSF) for UNIGE and EPFL and the State Education and Development Agency (VIAA) of Latvia for RSU; the Defitech Foundation (Morges, Switzerland) and by the Bertarelli Foundation (Catalyste Program, Gstaad, Switzerland). Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = sep,

doi = "10.1016/j.clinph.2023.06.009",

language = "American English",

volume = "153",

pages = "57--67",

journal = "Clinical Neurophysiology",

issn = "1388-2457",

publisher = "Elsevier Ireland Ltd",

}

Windel, F, Gardier, RMM, Fourchard, G, Viñals, R, Bavelier, D, Padberg, FJ, Rancans, E, Bonne, O, Nahum, M, Thiran, JP, Morishita, T & Hummel, FC 2023, 'Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation', Clinical Neurophysiology, vol. 153, pp. 57-67. https://doi.org/10.1016/j.clinph.2023.06.009

TY - JOUR

T1 - Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

AU - Windel, Fabienne

AU - Gardier, Rémy Marc M.

AU - Fourchard, Gaspard

AU - Viñals, Roser

AU - Bavelier, Daphne

AU - Padberg, Frank Johannes

AU - Rancans, Elmars

AU - Bonne, Omer

AU - Nahum, Mor

AU - Thiran, Jean Philippe

AU - Morishita, Takuya

AU - Hummel, Friedhelm Christoph

N1 - Funding Information: We acknowledge access to the facilities and expertise of the MRI facility of the Human Neuroscience Platform (HNP) of the Foundation Campus Biotech Geneva supported by the University of Geneva, Geneva University Hospitals and the École Polytechnique Fédérale de Lausanne (EPFL). We further thank Sylvain Harquel for his input regarding the statistical analysis of the data. Funding Information: This work was supported by ERA-NET NEURON (The DiSCoVer project). The NEURON ‘Network of European Funding for Neuroscience Research is established under the organization of the ERA-NET ‘European Research Area Networks’ of the European Commission. National funding agencies are the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF] ) for LMU Munich, the Ministry of Health (MOH) for HUJI and Hadassah, the Swiss National Science Foundation (SNSF) for UNIGE and EPFL and the State Education and Development Agency (VIAA) of Latvia for RSU; the Defitech Foundation (Morges, Switzerland) and by the Bertarelli Foundation (Catalyste Program, Gstaad, Switzerland). Publisher Copyright: © 2023

PY - 2023/9

Y1 - 2023/9

N2 - Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-to-use digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants’ placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients’ daily lives.

AB - Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-to-use digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants’ placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients’ daily lives.

KW - Computer vision

KW - Electrode localization algorithm

KW - Home-based non-invasive brain stimulation

KW - Monitoring

KW - Real-time feedback

KW - tES

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U2 - 10.1016/j.clinph.2023.06.009

DO - 10.1016/j.clinph.2023.06.009

M3 - Article

C2 - 37454564

AN - SCOPUS:85165056822

SN - 1388-2457

VL - 153

SP - 57

EP - 67

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

ER -

Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

Abstract

Bibliographical note

Keywords

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